| { |
| "paper_id": "C96-1038", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T12:52:05.871105Z" |
| }, |
| "title": "A Rule-Based and MT-Oriented Approach to Prepositional Phrase Attachment", |
| "authors": [ |
| { |
| "first": "Kuang-Hua", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Hsin-Hsi", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "I'rel)ositional l'hrase is the key issue in structuraJ ambiguity, l{ecently, researches in corpora provide the lexical cue of pre[)ositions with other words and the information could be used to l)artly resolve ambiguity resulted from prepositionM phrases. Two possible a~t.achments are considered in the lit, erature: either I]OUll attaehluelll[, or verb attachment. In this paper, we consider the problem fl'om viewpoint of m~> chine translation. Four different attachments arc told out according to their ['unctiona.lity: llOU[l attatchmellt, verb at.ta.clllllellt, sentence-level att&chlneut, ~md predicate-level attachtllelll,. [~oth lcxi(:al k.owledge and semantic knowledge are involved resolving atta.chment in tl~e proposed me(:hanism, t';xperimental results show that considering more types of prepositional phrases is useful in machine translation.", |
| "pdf_parse": { |
| "paper_id": "C96-1038", |
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| "abstract": [ |
| { |
| "text": "I'rel)ositional l'hrase is the key issue in structuraJ ambiguity, l{ecently, researches in corpora provide the lexical cue of pre[)ositions with other words and the information could be used to l)artly resolve ambiguity resulted from prepositionM phrases. Two possible a~t.achments are considered in the lit, erature: either I]OUll attaehluelll[, or verb attachment. In this paper, we consider the problem fl'om viewpoint of m~> chine translation. Four different attachments arc told out according to their ['unctiona.lity: llOU[l attatchmellt, verb at.ta.clllllellt, sentence-level att&chlneut, ~md predicate-level attachtllelll,. [~oth lcxi(:al k.owledge and semantic knowledge are involved resolving atta.chment in tl~e proposed me(:hanism, t';xperimental results show that considering more types of prepositional phrases is useful in machine translation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "Prepositional phrases are usually ambiguous. The wen-known sentence shown in the following is a good example.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "Kevin watched the girl with a telescope. Whether the prepositionM phre~se with a telescope modifies the hea.d noun girl or the verb watch are not resolved by using only one knowledge sour(:e. Many researchers observe text corpora and learll some knowledge based on language model I,o determine the plausible attachment. For example, we could expect that the aforementioned prepositional phrase is usually attached to verb according to text corpora.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "However, the col rect attachment is dependent on world knowledge SOlfleti/TleS. Some al)l)roaches to determination of Pl)s arc rel)orl,cd in literature. (Kimball, 1973; Frazier, 1978; Ford et al., 1982; Shieber, 1983; Wilks ct al., 1985; Liu et aJ., [990; (',hen and C:hcn, [992: Ilindle and Rooth, 1993; |bill and l{esnik, 1.994) . 'l.'he possible attachment they conskler are NO UN attachment and VI.]RJ/ aU;aehment. These resolutions fall into three categories: sy.tax-based, semantics-based and corpus-based a.pproaches. The brief discussion are described in the following:", |
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| { |
| "start": 153, |
| "end": 168, |
| "text": "(Kimball, 1973;", |
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| "start": 169, |
| "end": 183, |
| "text": "Frazier, 1978;", |
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| "start": 184, |
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| "text": "Ford et al., 1982;", |
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| "start": 203, |
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| "text": "Shieber, 1983;", |
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| "start": 218, |
| "end": 237, |
| "text": "Wilks ct al., 1985;", |
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| "start": 238, |
| "end": 255, |
| "text": "Liu et aJ., [990;", |
| "ref_id": null |
| }, |
| { |
| "start": 256, |
| "end": 304, |
| "text": "(',hen and C:hcn, [992: Ilindle and Rooth, 1993;", |
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| "start": 305, |
| "end": 330, |
| "text": "|bill and l{esnik, 1.994)", |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "I. Syntax-tx~sed", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "\u2022 Right Association (Kimball, 1973) The PPs ;dwa.ys modifies the nearest component pre(:eding i t. \u2022 Minimal At.t.achmcnl. (Frazier, 1978: Shieber, 1!.) 83) The correct attaching point of' a 1'1 ) in a sentence is determined by l,he nmnber of nodes in a p~ursing tree.", |
| "cite_spans": [ |
| { |
| "start": 20, |
| "end": 35, |
| "text": "(Kimball, 1973)", |
| "ref_id": null |
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| { |
| "start": 99, |
| "end": 152, |
| "text": "\u2022 Minimal At.t.achmcnl. (Frazier, 1978: Shieber, 1!.)", |
| "ref_id": null |
| } |
| ], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 l,exicM Pret>rence (Ford et a.l., ]982)", |
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| "eq_spans": [], |
| "section": "Semantics-based", |
| "sec_num": "2." |
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| "text": "The real attaching point llulst S;'l..tist~ r some constraints, e.g., w~rb ligatures. I)it[~rent verbs accolnl)anying with the same PPs may have the different att.~u:hlug points. \u2022 l're, fi~rence Semantics (Wilks et al., 1!) 85)Wilks amd his colleagues argue the real attaching l)oint must be determined lay the preference of verbs a.nd prepositions. \u2022 Propagated Semantics ((:hen a.nd (7:hen,", |
| "cite_spans": [ |
| { |
| "start": 206, |
| "end": 220, |
| "text": "(Wilks et al.,", |
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| "start": 221, |
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| "text": "1!)", |
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| "section": "Semantics-based", |
| "sec_num": "2." |
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| { |
| "text": "The attachment of prepositional l)hrasc is co-determined by the semantic usage of noun, w~rt), attd preposition.", |
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| "section": "1992)", |
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| "text": "\u2022 Sta.dstical Score (I,iu at a.l., 1990) They use semantic score and synta.ctic s(x)re t,c) (]el,l'rnfitie l;h,+ ai,l,a/:hing i)(>iut,+ 'l'[l(<s(, , , 4(.c)1'c, , , 4 Ftl'(~ (, l'+~, ill(+(I [1'() 111 Lexl, /:c)r +. ])()l'a.", |
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| "start": 165, |
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| "text": "4 Ftl'(~ (,", |
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| "start": 177, |
| "end": 182, |
| "text": "l'+~,", |
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| { |
| "start": 183, |
| "end": 196, |
| "text": "ill(+(I [1'()", |
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| "section": "Corpus-based", |
| "sec_num": "3." |
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| "text": "\u2022 I,c\u00d7i,a.I A++.'+<wia+l.icm (tlindle a.ud I{oc+t+h, 1,9!)3) Thi,~ nl0i,hod a,l)l)li ('s sl,al,isl,ical l.e< [l(~ (:Oiill[,(;] ' ex~unl)les even iu the sinll)lc .setil,(~ii(:es like %here is a, I)ook oil l, he l, alde\" a.ud %he at)l)lc lias wol:ui in il?', lu l.he tirsl, e\u00d7anil)le, Lhe PI' \"ou i,lie t:al)h'\" is nei-l,her used 1,o iiiocti[y I,he COl)Ula verl) ilor l;h(\" llt)llli i/lirase \"a book\", li, (lescrii)e,~ l,he sil,ual.h)n (d' the whole Hi;it[,C~ltCCX The se(:oud exa.iiil)[C' ,~lio;vs Lha.(, t, he ]Tp \"hi it\" is also nor a nio(lifiet +, but, a, ~:otuple-tiiOU/, LO t]lC' i)rocedhl~ nOtlIl l)hrase. 'l'ha.L is, die I>I 7 has a nOlll:esLricl,ivl:: IlS~/,g(;. 'ro l;ra, tls[er 17ps alttong (liff'er(mi, la.ngu~ges, we iiitisl, (';I,l)l, lii;t; tim ('ori'('(% inl:erl)ret,a.i;ion. 'l'here[+ore, wc (list, iugui~h [our difl'ereni, pl:l,i) osii,ional I)]ll'a+~t~s.", |
| "cite_spans": [ |
| { |
| "start": 85, |
| "end": 108, |
| "text": "('s sl,al,isl,ical l.e<", |
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| }, |
| { |
| "start": 109, |
| "end": 126, |
| "text": "[l(~ (:Oiill[,(;]", |
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| { |
| "start": 790, |
| "end": 846, |
| "text": "'l'here[+ore, wc (list, iugui~h [our difl'ereni, pl:l,i)", |
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| "section": "Corpus-based", |
| "sec_num": "3." |
| }, |
| { |
| "text": "\u2022 Ih'edh:ai, ivc 1715~ (151717): I)IM i hai ~('rve a,s I~red i(:a.l;es. He is at home.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Corpus-based", |
| "sec_num": "3." |
| }, |
| { |
| "text": "He found a lion in the net. I-a1 falxian4 shilzi5 zai4 wang3zi5 li3.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tal zai4 jial.", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Sem,emial I>IM (,~1>1>) \u2022 I)1<'+ Lhal, ,~erve rm,,l.ic)us of l.iln(\" a.u\u00a2l hx:a.d<m.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Tal zai4 jial.", |
| "sec_num": null |
| }, |
| { |
| "text": "[here is no parking along the street. Zhe4 tiao2 jiel shahs4 jin4zl~i3-ting2 chel. We had a good time in Paris. Zai4 balli2 wo3men5 you3 yil duan4 mei3hao3 de5 shi2guangl,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tal zai4 jial.", |
| "sec_num": null |
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| { |
| "text": "\u2022 As %r SPP, VPP, and NPP, the rules are dependent on the lexical knowledge and semantic usage. That is to say, the semantic tag should be assigned to each word. Figure 1 and Figure 2 describe the semantic hierarchy for noun and verb. ltowever, rnammlly building a lexicon with semantic tag information is a time-consuming and humanintensive work. Fortunately, an on-line thesaurus provides this information, l{oget's thesaurus defines a semantic hierarchy with 1000 leaf nodes shown in 'fable 1. l!]ach leaf node contain words with this semantic usage, that is, these words haw~ the semantic tags rel-)resented by these leaf nodes. We just map these leaf nodes to the senlantic definitions listed in Figure 1 and Figure 2 . Therefore, nouns and verbs in running texts could be easily assigned semantic tags in our semantic definitions.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 162, |
| "end": 170, |
| "text": "Figure 1", |
| "ref_id": "FIGREF4" |
| }, |
| { |
| "start": 175, |
| "end": 183, |
| "text": "Figure 2", |
| "ref_id": "FIGREF4" |
| }, |
| { |
| "start": 701, |
| "end": 709, |
| "text": "Figure 1", |
| "ref_id": "FIGREF4" |
| }, |
| { |
| "start": 714, |
| "end": 722, |
| "text": "Figure 2", |
| "ref_id": "FIGREF4" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Tal zai4 jial.", |
| "sec_num": null |
| }, |
| { |
| "text": "In general, four factors contribute the deterruination o[\" PP-attachment: ]) verbs; 2) a<'cusativc nouns; 3) prepositions; and 4) oblique nouns.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tal zai4 jial.", |
| "sec_num": null |
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| { |
| "text": "We use a 4-tuple <V, N1,P, N2} to denote the relationship of a possible PP attachment, where V denotes semantic tag of verbs, N] denotes the semantic tag of accusative noun, P denotes the preposition and N2 denotes the semantic tag of obliqne noun. For example, the following sentence has the 4-tuple {non_speech_act, human, with, in.strurncnt}.", |
| "cite_spans": [], |
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| "section": "Tal zai4 jial.", |
| "sec_num": null |
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| "text": "Having the 4-tuple in advance, we could apply 67 rule-templates listed in Appendix to determine what the PP type is by aforementioned steps. That is, apply SPP rule-template tirst, and then VPP rule-template. If none succeeds, the PP should be an NPP. We summarize tile algorithm as follows.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Kevin watched the girl with a telescope.", |
| "sec_num": null |
| }, |
| { |
| "text": "(1) Check if it is a PPP according to the predicate-argument structure.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithln 1: Resolution to PP-Attachlnent", |
| "sec_num": null |
| }, |
| { |
| "text": "(2) Check if it is an SPP according to 21 ruletemplates tbr SPP.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithln 1: Resolution to PP-Attachlnent", |
| "sec_num": null |
| }, |
| { |
| "text": "(3) Check if it is a VPI' according to 46 ruletemplates tbr VPP.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithln 1: Resolution to PP-Attachlnent", |
| "sec_num": null |
| }, |
| { |
| "text": "(4) Otherwise, it is an NPI'.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithln 1: Resolution to PP-Attachlnent", |
| "sec_num": null |
| }, |
| { |
| "text": "The Penn Treebank (Marcus et al., 1993) is used as the testing corpus. The following is a real example extracted from this treebank. )))))))))) .)", |
| "cite_spans": [ |
| { |
| "start": 18, |
| "end": 39, |
| "text": "(Marcus et al., 1993)", |
| "ref_id": "BIBREF10" |
| } |
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| "eq_spans": [], |
| "section": "Experiments", |
| "sec_num": "4" |
| }, |
| { |
| "text": "The PPs contained in Penn 'IYeebank are collected and associated with one label of PPP, S1)P, VPP, or NPP. For example, the Pl)s contained in the atbrementioned sentence are extracted as tblh>ws.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experiments", |
| "sec_num": "4" |
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| "text": "(from the IIampton County jail, V P P) (in SpringJ'icld, N PP) (with a special jack, VPP} (on their home phonc~, NPP) ']'hese extracted PPs constitute the standard set and then the attachment algorithm shown in previous section are applied to attaching these PPs. Finally, the attached PPs are compared to the standard set for perff)rmance evaluation. The resuits are shown in Table 2 First, NPP and VPP dominate the distribution of PPs (92%). The former occupies 49% popula tion and the latter 43%. If' we carefully process NPP and VPP, tile result would be good. In fact, the proposed algorithm is based on the philosophy of model refinement. That is, we assume~ each PP is NPP except it ix a I)PP or it matches tim 67 rule-templates. N I I,I,1,1, , 1 I\"0rma, l,ion () F I: I,',( 7 I'10 NS ,qym l) -.'mc'Idal(::.:/,cost, have, own) st.,to +rm:~tal (c, :t.kno'w, tlrMk, lik:) li, .ki., :/(c 4/. b.co, mc , :/'ro., , look) perccpt io~.( c .t/.sc c , t., st. , J'c H ) . 'u'~dcrstand, cchacl(c..q..~ay, tell, stale) ac.! -mental --.SlWechacl(c.g.calch, , hil, kill) aclion {+me,nlal(i.!l.re,meml~cr, learn,rcadc) -t. montion( e.g.come, fall, :/o) acli'vily -.me'nlal -moniio,n(e.g.wo'rk, drive, draw) ,in,ttc -4~,umcr~(e.:l.cctt) insLrmm~nl (e.:l.hammcr) -,,,,imal, e object(e.:l.card) vch, ide,(,..9.c~rr) \"mct~,;r( c.9.'wcty) location ((.:l.bookslm ( c .~/ . j',ct ) c'l~ct~t (c.q.ca'rl/~.quahc) m,o'.,tio'iQ 4/.:ra\",.s f er) -.adu 't*umbeT'(c.g.dozen) product ( c 41.'wr'it i'n!/ ) 'rel i:/io'n,( c .g .he.ave'n) sc.,s.,t, ion, (e. 41 .P\"i\" )", |
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| "text": "tell,", |
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| "text": "stale)", |
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| "text": "--.SlWechacl(c.g.calch, , hil, kill)", |
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| "text": "{+me,nlal(i.!l.re,meml~cr, learn,rcadc) -t. montion( e.g.come, fall, :/o) acli'vily -.me'nlal -moniio,n(e.g.wo'rk, drive, draw)", |
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| { |
| "start": 1200, |
| "end": 1228, |
| "text": ",in,ttc -4~,umcr~(e.:l.cctt)", |
| "ref_id": null |
| }, |
| { |
| "start": 1240, |
| "end": 1305, |
| "text": "(e.:l.hammcr) -,,,,imal, e object(e.:l.card) vch, ide,(,..9.c~rr)", |
| "ref_id": null |
| }, |
| { |
| "start": 1336, |
| "end": 1349, |
| "text": "((.:l.bookslm", |
| "ref_id": null |
| }, |
| { |
| "start": 1350, |
| "end": 1367, |
| "text": "( c .~/ . j',ct )", |
| "ref_id": null |
| }, |
| { |
| "start": 1377, |
| "end": 1426, |
| "text": "(c.q.ca'rl/~.quahc) m,o'.,tio'iQ 4/.:ra\",.s f er)", |
| "ref_id": null |
| }, |
| { |
| "start": 1433, |
| "end": 1453, |
| "text": "'t*umbeT'(c.g.dozen)", |
| "ref_id": null |
| }, |
| { |
| "start": 1462, |
| "end": 1514, |
| "text": "( c 41.'wr'it i'n!/ ) 'rel i:/io'n,( c .g .he.ave'n)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 377, |
| "end": 384, |
| "text": "Table 2", |
| "ref_id": "TABREF4" |
| }, |
| { |
| "start": 737, |
| "end": 770, |
| "text": "N I I,I,1,1, , 1 I\"0rma, l,ion ()", |
| "ref_id": "FIGREF4" |
| }, |
| { |
| "start": 771, |
| "end": 799, |
| "text": "F I: I,',( 7 I'10 NS ,qym l)", |
| "ref_id": "FIGREF4" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Experiments", |
| "sec_num": "4" |
| }, |
| { |
| "text": "vol il ion ( c,g.'.:ill ) l,'igurc 2: Sommltics 'FaDs for Nouns. not trtisdet, ermine(l and this corresponds to the behavior l,o model refitmnletg,. Ilowever, tmmy VI)Ps a.re no/ correctly resolved due l,o t.he rigidity of rulo-t.enll)les. ThereFore, relaxing these rules will resuli, hi nlore correct, \\/lq >` I)ul less <:orre<:t NPP.", |
| "cite_spans": [ |
| { |
| "start": 11, |
| "end": 25, |
| "text": "( c,g.'.:ill )", |
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| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
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| "sec_num": "4" |
| }, |
| { |
| "text": "knol,her dil/h:ult, y COllieS ['ton1 (,he assignnieiit of Seillalli.ie Lags. As everyolle kliOWS Lhe SeliSe &inlJi guity is a. serious l)roi)leni, Lo assign Uli ique setnantic i ag is hard. We l)la.n to resolve this i)rol)leni in l,he iiea, r flit, life alld Lo lise I,he s+qnaiiti('-i;agged <:ort)us 1,o t, rain lhe rule-t;<~Uil)lai,es+ (__,__,after, lime) \u2022 t. (__,__, alert+l, location) 5. ( .... a?no+~q, loc'atio++ 6. ( .... ,,,z, {lo~:atio,,,/,im< }) 7. ( .... before, time) ,~. ( .... txtt,,~+n, {location, 12. (__, __, m_.front_.of, locaZion) 13. (__,', near, Iocatirm) I(;. ( ,m~,l'i,m .... @, {/or.tim,,ob.~, ,l}) 17. ( .... , I,:~, {.I,,.~,..~ ~,, ,:,,, ,,~ ,/, i, ,~, ,', /,\" ~, } ) 18, ( .... b:q, .nimal __, _, .f ,,,, {.1,,.~.,..,,~,, , ,,, ,~, ~,t,y I.,,.,,,,,,,.~.,}) 2 ~. ( { ,,,,,ti,.~, .~p, , ,,/,_.,,~ }, --, J,,. ,, ,.,,~ ", |
| "cite_spans": [ |
| { |
| "start": 338, |
| "end": 357, |
| "text": "(__,__,after, lime)", |
| "ref_id": null |
| }, |
| { |
| "start": 363, |
| "end": 389, |
| "text": "(__,__, alert+l, location)", |
| "ref_id": null |
| }, |
| { |
| "start": 393, |
| "end": 456, |
| "text": "( .... a?no+~q, loc'atio++ 6. ( .... ,,,z, {lo~:atio,,,/,im< })", |
| "ref_id": null |
| }, |
| { |
| "start": 460, |
| "end": 480, |
| "text": "( .... before, time)", |
| "ref_id": null |
| }, |
| { |
| "start": 485, |
| "end": 513, |
| "text": "( .... txtt,,~+n, {location,", |
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| }, |
| { |
| "start": 518, |
| "end": 550, |
| "text": "(__, __, m_.front_.of, locaZion)", |
| "ref_id": null |
| }, |
| { |
| "start": 555, |
| "end": 577, |
| "text": "(__,', near, Iocatirm)", |
| "ref_id": null |
| }, |
| { |
| "start": 583, |
| "end": 623, |
| "text": "( ,m~,l'i,m .... @, {/or.tim,,ob.~, ,l})", |
| "ref_id": null |
| }, |
| { |
| "start": 628, |
| "end": 636, |
| "text": "( .... ,", |
| "ref_id": null |
| }, |
| { |
| "start": 676, |
| "end": 694, |
| "text": ",~, ,', /,\" ~, } )", |
| "ref_id": null |
| }, |
| { |
| "start": 699, |
| "end": 717, |
| "text": "( .... b:q, .nimal", |
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| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 637, |
| "end": 675, |
| "text": "I,:~, {.I,,.~,..~ ~,, ,:,,, ,,~ ,/, i,", |
| "ref_id": "FIGREF4" |
| }, |
| { |
| "start": 718, |
| "end": 765, |
| "text": "__, _, .f ,,,, {.1,,.~.,..,,~,, , ,,, ,~, ~,t,y", |
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| }, |
| { |
| "start": 766, |
| "end": 784, |
| "text": "I.,,.,,,,,,,.~.,})", |
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| }, |
| { |
| "start": 790, |
| "end": 843, |
| "text": "( { ,,,,,ti,.~, .~p, , ,,/,_.,,~ }, --, J,,. ,, ,.,,~", |
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| }, |
| { |
| "text": "3(i.", |
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| "eq_spans": [], |
| "section": "35.", |
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| }, |
| { |
| "text": "3N.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "37.", |
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| }, |
| { |
| "text": "(l!).", |
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| "eq_spans": [], |
| "section": "37.", |
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| }, |
| { |
| "text": "I(}.", |
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| "eq_spans": [], |
| "section": "37.", |
| "sec_num": null |
| }, |
| { |
| "text": ".ll.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "37.", |
| "sec_num": null |
| }, |
| { |
| "text": "1,1.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "13.", |
| "sec_num": null |
| }, |
| { |
| "text": "15.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "13.", |
| "sec_num": null |
| }, |
| { |
| "text": "[G. (m~,l i,m,__, i., {Io~'.li... i~/rm., ~l } } ( ..... i., ~,,hidl } (.~ 'l,__, li/,~ ,-- ~,,, t,~, {l,,,..~i~,,,, ~,6.j,,.t }) . b,~lr.~'t, ,~&i, ,'t } ) . ", |
| "cite_spans": [ |
| { |
| "start": 4, |
| "end": 74, |
| "text": "(m~,l i,m,__, i., {Io~'.li... i~/rm., ~l } } ( ..... i., ~,,hidl } (.~", |
| "ref_id": null |
| }, |
| { |
| "start": 132, |
| "end": 156, |
| "text": "b,~lr.~'t, ,~&i, ,'t } )", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 75, |
| "end": 91, |
| "text": "'l,__, li/,~ ,--", |
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| }, |
| { |
| "start": 92, |
| "end": 129, |
| "text": "~,,, t,~, {l,,,..~i~,,,, ~,6.j,,.t })", |
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| } |
| ], |
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| ], |
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| "raw_text": "II. I<,]h'-t.(+~Ht)htt(> for \\;l'I )", |
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| "raw_text": "II, (.I.m, tH, {.b.~lrm/,,v(,H} ./'g,r, /' .,./, .,,,/i,,., }) 6cl, wt ~ ~, little} 15. ( ......", |
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| "text": "li liiie se/!l,i()ii,s whal roliow,~, wc, will fir.st, F, rt~,senl, oilr viewt)oiui, hx)ttl tiin.lJiitie I,l'aliSl+!t;ion (,o (,his l)rol)lelii. ~e('(;ioii :l will c[is(:u:-+s i.[le ([tfl, ai[ rc.,,+;o-]liLioli I,c) I'Ps a+t.l;achilieU(;, whMi coiisi(ler.s iiiore cli[/'('reul; ill:l;a.l:liliiolil;s. ~e<'l,loti ;1 will (:/)lt(hl(:t, eX i>l'riiiienl,s i,o iuvest,igat,(~ ouJ' al)prcm.ch. Sl'('I,iou ,~ will l)rovicle S(/lile couclu(Ihtg ix)itinrk,s.", |
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| "FIGREF1": { |
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| "text": "i, he viewl>oiui, c)t' lita.chhic! t;ralishii, iou, iu l)arl, i('ula.r, l'higlish-(',hhie,,+e tiialJiiiie l.ra.u,sial,ion (( ',hc'ii it.tic] (',hell, I!)!),~), 1,he illaiil slic>rl,('()iiiitl~ O1\" the a,l)l)roa.che,s uic'ni, ionlx[ in Iweviolis se('l, il)li is t, lu~i, LhtLy ~dl (:c)u+sicler eiLlitw pp,,+ lilo(lify liOilll:4 or I>1)~ niocIi[y vc'ri)s. All,hough 1717,+ usua.lly iuocli[y llOllth~ Of v(;rl>s, l:]ler(~ ~11'0 SOl", |
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| "text": "NTow, t.he l)r,:)l)bn) is ',vim.t. ('olml, il.uti's tit(' tll+'dl a,t].i,~tll ol\" ('a(:h ,st,el). Oxfor(I Ach, a.ttc+xl I+earuor',~ l)i<:t.ic)uat'y (OA I. I)) (llortd)y, 1989) ([efi.e,s :{2 diff'<went v/,rl)I);til,orns i,,> (les<:rihe i, he usi~ge of' verl/+, 'l'hc'so vm'l) l]'.h.tlieS a.r(' lil,:c sl,:elelx)n oi' a s(mt,(m(:(' ancl t.h(' /:onstituenLs a.r{: I, he fh'sh or sent, ence. (then a.nd (~hen (1[)!])4) havo l)t'ol)osod a mot, hoc[ tc)cloI.crnd-natx+ l, he l)rc,di(:id;e i/rl'glll't}('tll[, sl, rtt(:i, tn'(, c,[' ;+ S(~ll-Lelt(:(L The OALI)-(Mit.'(/ vc:H) t'ra, utcs a.re rc ga.rdc'd a,s I, he t)rittta, ry la, ngui+ge kuowh>dg(+ source i11t(I ilri] NI 5 parser and u [ittil.e-st.al.(, txtec}ianisil+ arc. (:(:,Ol)er;d,ively used t,o cl1't, erutitt(' I,[t(> l)htu +ihle l)r<'(lica.t.e iut'gtllilelH, strll('l.ttr1++ ()H<'e th+' l)l'l~c]i(:a.l~(>-;+i'gtilli('ltt, +(,rticl, tlr(~ ()[ it Selit, etl(:(> (:oit-I,a, hls l)rC,posi,iona.t l)hra,se, t,}m uu(h,rlyiug t)reposi-I, iotta,[ phrase is I)PI '.", |
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| "text": "}IIIIIIIIIlI{;8,LI()II)l Ideas 81{i 8!)!) -VOL]'I'ION--In(livi(luM .......", |
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| "text": "Sema, n{,ics Tags for Verbs. cn, tiQ/ -~-CO'II.CT'C'~ (: ' --CO71,C7':~:\u00a2; -~-h.,,~ n.ct', (~ ..q. boy) +a,", |
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| "text": ".~ ) I)a~ivc voi(:{' 19. (__, ..... l'.r,/,i'm.c) 2{}. (m.~t.io. ..... for,/o~:..t~:o~) :r~. (", |
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| "FIGREF7": { |
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| "text": ",.~ }) :~':~. ( ......", |
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| "text": "is al.i.a.che,.l is ],:u()v:u and il.s ('orresl>c>n(I iu Z l>OSit, k)n in (thhlese is al.so det.,:>rtt[hl<xl.", |
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| "content": "<table><tr><td colspan=\"2\">Pl>s Modifying Verl),+ (VPP)</td></tr><tr><td colspan=\"2\">I went to a movie with Mary.</td></tr><tr><td colspan=\"2\">Wo3 ham ma31i4 qu4 kan4 dian4ying3,</td></tr><tr><td colspan=\"2\">I bought a book for Mary.</td></tr><tr><td colspan=\"2\">Wo3 wei4 ma31i4[ maiS/eb yi4 ben3 shul.</td></tr><tr><td colspan=\"2\">\u2022 Pit+-+ Mo(lirying Nouns (NISI 7)</td></tr><tr><td colspan=\"2\">-[he man with a hat is my brother.</td></tr><tr><td colspan=\"2\">.Dai4 mao4zi5 de5 ren2 shi4 wo3 gelgeS.</td></tr><tr><td colspan=\"2\">Give me the book on the desk.</td></tr><tr><td colspan=\"2\">Ba3 zhuol shang4 de5 shul gei3 wo3.</td></tr><tr><td colspan=\"2\">It, is ohvkms t,lla.t, (,h(>s(' four (lifl'('r/>nl, i)r/,l>(,si</td></tr><tr><td colspan=\"2\">i.i(mal l)hras+,s havo t,h('h' ~),,vN nt:,l)r(>l)ria.i.(, i>,:,~i</td></tr><tr><td colspan=\"2\">t,h)ns in (,h\" rose. Tim.I, is +d'i,er we (]et,(+l'ttiillc, t,lm</td></tr><tr><td colspan=\"2\">I,Yl)e of a l)rel)O,sit, iotml l)hra,~c, 1,11/' r(/n,sl,it, u(,ut, t,()</td></tr><tr><td>whMl 171 ' 3 Resolving</td><td>Pl. >-ALtachlneni;</td></tr><tr><td colspan=\"2\">In t.he l)revi()us me('l.km, ['our clif[(woul, t.ylw~ eft'</td></tr><tr><td colspan=\"2\">P['s m'e de[in('d a.\u00a2x:ordin Z 1.o l.hdr I'tln('t.i(malit.5.</td></tr><tr><td colspan=\"2\">'l'hus, t.hc, reso[ul.iou 1.o thi~ t)rc>l)h~n~ i~ I.(i (hq.('r</td></tr><tr><td colspan=\"2\">miue whiHl tyF, e the I:'l)m h(d(mg 1.o. 'rim I~a.+i(.</td></tr><tr><td>st.el),S ~1 i'{,:</td><td/></tr></table>", |
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